A common type of scintillation camera is schematically shown in FIG. 1. A lesion 15a of a patient body 15 containing a radioactive pharmaceutical emits gamma rays 16. The camera includes a collimator 13 so that only gamma rays within a predetermined narrow angle from the lesion 15a can pass through the collimator 13 to a scintillation crystal 11. A single gamma ray 16 entering the scintillation crystal 11 causes a scintillation event 17, where scintillation light 18 radiates outwards through a glass light guide 12 to an array of photomultiplier tubes (PM tubes) 10. The PM tubes 10 can be arranged in a rectangular or hexagonal grid. When a scintillation event occurs, each PM tube detects the scintillation light 18 and produces electrical signals, whose intensity is related to the amount of light received and which is transmitted to a signal processing unit 14. The unit 14 analyses all the signals received from the individual PM tubes in order to determine the position of the scintillation event 17, i.e., where each gamma ray 16 impinges on the scintillation crystal 11.
Several different algorithms have been used to calculate the incident location of the gamma-ray, i.e., the scintillation event position as discussed above. One commonly used algorithm is the centroid algorithm. The first step of the centroid algorithm is to calculate the centroid, or the intensity weighted averaged position of the scintillation event. This is calculated by summing the product of the position of each PM tube and its energy response to an event, and dividing this sum by the sum of the intensities of each PM tube.
The centroid algorithm is affected by the non-linearity of the relationship of amount of light received at a particular distance from the scintillation event, and the position and consequently the resulting image is highly distorted. This centroid calculation is very approximate, and results in events being weighted toward the centre of the PM tube under which the event happened. As such, it is not clinically useful without further corrections. However, the correction involves a complex procedure to make a correction table. Further, there remain residual distortions in the image even after corrections are applied.
Likelihood methods have also been used in the past. In general, likelihood methods require iterative calculations, which are computationally intensive, and so not practical for use in a real-time situation. Specifically, with this algorithm, a position is first guessed, then, through rigorous iteration, it is made better, at each step of computing the probability (likelihood) of the event to have taken place at that position, considering the individual PM tube outputs. This gives rise to very good spatial and energy resolution, but at the expense of an unbearably slow processing pace. For example, the maximum likelihood algorithm processes less than 100 events a second, due to the complexity of the computations (involving squares and logarithms), and to the iterative nature of the algorithm.
Another difficulty with conventional algorithms is that even larger distortions occur when an event occurs closer to the edge of the camera than in other regions, which means that these events cannot be used in analysis. Effectively, this results in inefficient use of the crystal surface. In some cases, 20% or more surface area around the edge of the camera is wasted space. The useable 80% or less may not be sufficient to obtain required views. As well, patient comfort is compromised when the camera cannot easily be positioned as required.
Therefore, there is a need to provide a new positioning algorithm, which can reduce the quantity of data processed by the camera electronics, retain the speed for practical use in real time, more effectively use the crystal surface, and reduce the need for corrections producing a higher quality image and better patient diagnoses.